The present invention relates to medical instruments, and more particularly, but not exclusively to medical instruments for the detection and analysis of High Frequency ECG (electrocardiograph or “ECG”) signals.
ECG describes the electrical activity of the complex of muscles that make up the different chambers of the heart. An ECG signal is recorded by body surface electrodes or implantable electrodes that measure the change in electrical potentials over the body due to the propagating electrical activation in the heart.
The ECG signal is a vector, that is to say it has directional properties. The different parts of the heart are at different locations, and signal propagation across the body is slow relative to the rate of events in the ECG, so that the overall waveform seen at different locations shows the different components of the overall ECG signal in different relationships with the other components.
Traditionally, up to ten detector electrodes are used, positioned at selected locations, so as to capture what is known as a 12-lead electrocardiogram. The basic ECG is captured by a single lead or electrode.
FIG. 1 depicts a typical ECG signal waveform, acquired by such an electrode. The waveform is generally divided into the following components as illustrated. The P wave 101, describes the depolarization of the atria, the QRS complex 103, describes ventricular depolarization, and the T wave 105, describes ventricular repolarization. Irregularity in these components is taken as a sign of heart problems.
ECG signal acquisition is most commonly performed while the person being monitored is at rest. However, since physical stress is known to introduce features into the ECG signal indicative of coronary artery disease (CAD) not present in signals obtained at rest, an ECG signal may also be obtained from a subject during a stress test comprising phases of rest, exercise and recovery from exercise. Certain medical procedures, especially catheterization of coronary arteries, are performed while the ECG signal is continuously monitored in order to ascertain the heart condition during the procedure.
Information related to the heart activity is extracted by means of ECG inspection and analysis, which concentrates on what is known as the P-QRS-T segment of the signal, as shown in FIG. 1. With the exception of the identification and interpretation of cardiac arrhythmias, most of the commonly used diagnostic aids based on ECG data, such as an S-T segment 111 shift, prolonged and bizarre QRS complex 103 patterns, or T wave 105 inversion—as indicated by their names—are related primarily to inspection of the P-QRS-T segment of the signal.
The significant frequency range of the ECG signals was traditionally considered to be from 0.05 Hz to 100 Hz. Although many common diagnostic methods are based solely on information contained in the 0.05 Hz-100 Hz frequency range, valuable information is known to be found in higher frequencies in the range of 150 Hz-250 Hz.
In “High-Frequency Electrocardiogram Analysis of the Entire QRS in the Diagnosis and Assessment of Coronary Artery Disease” article, published in the Progress in Cardiovascular Diseases journal, Vol. XXXV, No. 5, March/April 1993, the contents of which are hereby incorporated by reference, Abboud et al describe a study of the correlation between a decrease in the high frequency component of the QRS complex of an ECG signal, and an ischemic condition of the heart. Abboud at all have defined a condition of reduced amplitude zone (RAZ), in which there is a deep trough in the center of the envelope of the high frequency QRS signal for animals and persons undergoing an ischemic event.
Reference is now made to FIG. 2 which is a comparative diagram that illustrates traditional ECG and high frequency ECG signals obtained during different stages of a stress test of an ischemic heart disease (IHD) patient 210, compared with traditional ECG and high frequency ECG signals obtained during different stages of a stress test of a healthy subject 220.
The upper part 210 of the figure represents a typical example of the ECG signal during different stages of a stress test of an ischemic patient. The first row in the figure indicates the heart rate. The second row presents the standard ECG signal and the third row presents the HF signal. The HF signal shows a significant change as the exercise test progresses. The marked decrease in the amplitude of the signal is particularly notable.
The lower part 220 of FIG. 2 represents a typical example of the ECG signal during a stress test for a healthy subject. As in upper part 210, it is possible to follow the evolution of both the standard ECG and the HF signals during the test. Unlike Upper part 210, no significant change in the amplitude of the HF signal can be detected, indicating that no ischemic episode has occurred.
The problem posed by the present inventors was how to distinguish in automatic manner between the case of upper part 210 and lower part 220.
US Patent Applications 20030013978 by Schlegel et al. and 20040039292 by Schlegel et al. disclose RAZ analysis of the high frequency waveform.
The high frequency ECG signal is more difficult to process compared to the standard low frequency ECG signal, obtained in the range of 0.05-100 Hz. While the low frequency signal level is located in the millivolt range, the high frequency signal level is up to three orders of magnitude lower in voltage, and is highly sensitive to the fitness of the electrode-body contact and variations in such contacts during the ECG signal acquisition. Furthermore, motion of the body organs and muscles, especially while performing a stress test, reduces further the high frequency signal to noise ratio.
U.S. Pat. No. 7,151,957 to Beker et al., the contents of which are hereby incorporated by reference, discloses methods of high frequency waveform averaging to obtain an improved signal to noise ratio from such a signal.
Beker et al (“Analysis of High Frequency QRS Potential during Exercise Testing Patients with Coronary Artery Disease and in Healthy Subjects”, Biomedical Engineering Department, Faculty of Engineering, Tel-Aviv University, 1995), and Abboud et al (Analysis of High Frequency Mid-QRS Potentials vs ST segment and T Wave Analysis for the Diagnosis of Ischemic Heart Disease, IEEE Computers in Cardiology 2003; 30:813-814), the contents of which are hereby incorporated by reference, showed that a decrease of the high frequency signal of the QRS complex during exercise test may serve as an indicator for an on-line early detection of ischemic pathologies. However, no details and no teaching were provided regarding the specifics of the signal processing, nor is there any disclosure of how the results can be analyzed to discriminate between sick and healthy subjects.
Simpson, in U.S. Pat. No. 4,422,459, teaches a system which analyzes only the late portion of the QRS interval and early portion of the ST segment, and in an off-line fashion (i.e. from previously stored data) to indicate cardiac abnormalities, in particular the propensity for cardiac arrhythmia. The late portion of a QRS waveform of a post myocardial infarction patient contains a high frequency (40 Hz-250 Hz) signal tail which is indicative of a tendency toward ventricular tachycardia. The system in Simpson digitally processes and filters a QRS signal in a reverse time manner to isolate the high frequency tail and avoid any filter ringing which would otherwise hide the signal. In order to carry out such reverse processing, Simpson presupposes that the raw data is stored. Otherwise it would not be possible to carry out processing in reverse time order.
Albert et al., U.S. Pat. No. 5,117,833, partially focuses on analyzing signals within the mid-portion of the QRS interval for the indication of cardiac abnormality. The system of Albert et al. uses a previously known technique of building up data points to derive an average of heartbeat characteristics in order to enhance signal to noise ratio. Data are collected and filtered and then stored for subsequent analysis. Thus, the system does not teach a cardiac monitor which provides the data analysis immediately from the data derived from a patient.
Albert et al., U.S. Pat. No. 5,046,504, similarly teaches the acquisition of QRS data and subsequent analysis. Routine calculations are performed from the data previously calculated and stored. Further, Albert teaches producing a set of digital spectrum values representative of an approximate power density spectrum at each of a large number of generally equally spaced sampling time intervals of the ECG waveform.
Seegobin, in U.S. Pat. Nos. 5,655,540 and 5,954,664, provides a method for identifying coronary artery disease. The method relies on a previously formed database of high and low frequency ECG data taken from known healthy and diseased subjects. Comparison of the data leads to a “Score” component, indicating deviation of the ECG data from the norm. This reference is calculation intensive, and does not suggest monitoring the condition of a patient, but rather is utilized as an off-line diagnostic tool.
Hutson, U.S. Pat. No. 5,348,020, teaches a technique of near real-time analysis and display. The technique includes inputting ECG data from multiple, sequential time intervals and formatting those data into a two-dimensional matrix. The matrix is then decomposed to obtain corresponding singular values and vectors for data compression. The compressed form of the matrix is analyzed and filtered to identify and enhance ECG signal components of interest. As with other systems, this reference focuses on late potentials, a fraction of the QRS interval, as the tool to identify cardiac disease.
There is thus a widely recognized need for, and it would be highly advantageous to have an ECG system and method for the detection and analysis of heart disorder, for example ischemic events, which is devoid of the above limitations.